This study provides new insights into the potential use of machine learning in hydrological simulations.
Sporadic late-onset Alzheimer's disease (AD) is the most frequent cause of dementia associated with aging. Due to the progressive aging of the population, AD is becoming a healthcare burden of unprecedented proportions. Twenty years ago, it was reported that some indole molecules produced by the gut microbiota possess essential biological activities, including neuroprotection and antioxidant properties. Since then, research has cemented additional characteristics of these substances, including anti-inflammatory, immunoregulatory, and amyloid anti-aggregation features.
Alzheimer's Disease (AD) is a devastating neurodegenerative disorder of the brain, clinically characterised by cognitive deficits that gradually worsen over time. There is, at present, no established cure, or disease-modifying treatments for AD. As life expectancy increases globally, the number of individuals suffering from the disease is projected to increase substantially. Cumulative evidence indicates that AD neuropathological process is initiated several years, if not decades, before clinical signs are evident in patients, and diagnosis made.
Women represent ⅔ of the cases of Alzheimer's disease (AD). Current research has focused on differential risks to explain higher rates of AD in women. However, factors that reduce risk for AD, like cognitive/brain reserve, are less well explored. We asked: what is known about sex and gender differences in how reserve mitigates risk for AD?
The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power.
Objective: Many studies evaluated how the Magnetic Resonance Imaging (MRI) field strength affects the effectiveness to detect neurodegenerative changes of Alzheimer's disease (AD), derived from atrophy or thickness. To the best of our knowledge, no study evaluated before how tissue texture changes are affected. In this research, hippocampus texture features extracted from 1.5 T and 3 T MRI are evaluated how are affected by the magnetic field strength.
Exaggerations of the detrimental impact of recreational drug use on the brain is killing Black People.
Objectives: The mechanisms leading to neurodegeneration in Alzheimer's disease (AD) may involve oxidative stress and neuroinflammation. Ceruloplasmin (Cp) is a circulating protein that intersects both these pathways, since its expression is increased during the acute phase response, and the protein acts to lower pro-oxidant iron in cells. Since the role of Cp in AD, and its potential for use as a biomarker is not established, we investigated CSF Cp and its association with longitudinal outcome measures related to AD.
Clinical assessment of speech abnormalities in Cerebellar Ataxia (CA) is subjective and prone to intra- and inter-clinician inconsistencies. This paper presents an automated objective method based on a single syllable repetition task to detect and quantify speech-timing anomalies in ataxic speech. Such a technique is non-invasive, reliable, fast, cost-effective and can be used in the comfort of home without any professional assistance.